تقرير
Model Predictive Control for Neuromimetic Quantized Systems
| العنوان: | Model Predictive Control for Neuromimetic Quantized Systems |
|---|---|
| المؤلفون: | Sun, Zexin, Baillieul, John |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Electrical Engineering and Systems Science - Systems and Control |
| الوصف: | Based on our recent research on neural heuristic quantization systems, we propose an emulation problem consistent with the neuromimetic paradigm. This optimal quantization problem can be solved with model predictive control (MPC) by deriving the conditions under which the quantized system can guarantee (asymptotic) stability during emulation by optimizing a Lyapunov-like objective function. The neuromimetic model features large numbers of discrete inputs, and the optimization involves integer variables. The approach in the paper begins by solving an optimization using model predictive control (MPC) and then using a neural network to train the data generated in this process and applying Fincke and Pohst's sphere decoding algorithm to narrow down the search for the optimal solution. Comment: 6 pages |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2212.09887 |
| رقم الأكسشن: | edsarx.2212.09887 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |